Various engineering technologies have been developed for use in a wide variety of scientific and other environments, including but not limited to the fields of engineering, (geo)physics, biology, astrophysics, medicine, econometrics, military applications and analysis of economics and environmental and other sciences. The systems and technologies developed in these fields and others are often quite complex, thus requiring modeling techniques to monitor and analyze system performance. In particular, diagnostics techniques for detecting fault occurrence(s) by analyzing sensory information are often beneficial approaches to system modeling.
Diagnostic strategies are generally designed to compare parameters associated with an operational system to some sort of theoretical reference of the same parameters. More particularly, some diagnostic methods that assess signal behavior typically analyze system residuals, or errors between a system's actual measured behavior and that of a benchmark/reference condition quantifying normal or expected behavior. Most diagnostic strategies can be broadly characterized as either a Model-Based or Model-Free approach. In model-based methods, an analytical model generates the reference system conditions. Exemplary model-based implementations include parameter estimation (e.g., that provide equation and/or output error determinations), observer-based methods (e.g., Kalman residuals and fault filters), parity space equations, and transfer functions (e.g., frequency response). In contrast, model-free techniques primarily rely on the actual operational system parameters for referencing and diagnostics. Exemplary model-free implementations include heuristic and fuzzy methods (e.g., Bayesian and Decision Algorithms), vibrations analysis methods or signal-based methods (e.g., frequency domain methods such as Fast Fourier Transforms (FFTs) or wavelet transforms and limit checking) and learning methods (e.g., neural networks and stochastic methods such as those involving time series analysis).
Several specific distinctions can be drawn between model-based and model-free diagnostics. With particular reference to conventional model-based diagnostics, analytic redundancy of different signals ensures that each signal can be reproduced by an analytic model. The core aspect of model-based diagnostic techniques is a comparison between the model-generated signal and the actual signal.
With particular regard to conventional model-free diagnostics, signals are directly processed in the time or frequency domain to extract certain signal properties and subsequent analysis of those properties is then performed. Anomaly criteria are based on a static template of healthy system properties, thus disregarding the potential effects of actual system dynamics. The absence of a redundant reference in model-free diagnostic techniques can sometimes compromise the robustness of such technology.
Several issues have been identified as potential concerns with presently existing model-free and model-based diagnostic techniques. In general, some traditional models often fail to accurately describe the system behavior due to measurement uncertainties, nonlinear effects and oversimplified assumptions. Other more particular concerns are based on development limitations in conventional model-based diagnostics which focus on the introduction of signal-processing concepts (e.g., wavelet decomposition, fuzzy logic and Kalman filtering) to classical modeling approaches. Conventional analytical models were substituted by learning-based models to decrease modeling uncertainties. Diagnostics did not fully address the analogies between conventional dynamic concepts (e.g., impulse response and stability) and major stochastic properties such as autocovariance and stationarity. The only multivariate approach involves principle component analysis which typically disregards the system dynamic characteristics.
In light of existing techniques for analyzing system performance, a need remains for a technique that addresses two main fundamentals—whether or not system behavior has changed over time and whether two signals obey the same structure. These fundamentals are generally addressed in accordance with the presently disclosed technology by prognostics (describing and forecasting slow time drifts in system behavior) and diagnostics (detecting and defining system departures from normal or reference behavior). In one embodiment of the present technology, a rigorous statistical method and enhanced diagnostics tool minimizes misclassification errors and wrong conclusions and also applies multivariate time series techniques in assessing the intrinsic dynamics of a certain system.